期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2007
卷号：30
页码：565-620
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：We present a new algorithm for probabilistic planning with no observability. Our
algorithm, called Probabilistic-FF, extends the heuristic forward-search
machinery of Conformant-FF to problems with probabilistic uncertainty about both
the initial state and action effects. Specifically, Probabilistic-FF combines
Conformant-FF's techniques with a powerful machinery for weighted model counting
in (weighted) CNFs, serving to elegantly define both the search space and the
heuristic function. Our evaluation of Probabilistic-FF shows its fine
scalability in a range of probabilistic domains, constituting a several orders
of magnitude improvement over previous results in this area. We use a
problematic case to point out the main open issue to be addressed by further
research.
